Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations29165
Missing cells9027
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.1 MiB
Average record size in memory616.4 B

Variable types

Numeric7
Categorical11
Boolean2

Alerts

Has a mobile phone has constant value "1" Constant
Children count is highly overall correlated with Family member countHigh correlation
Employment length is highly overall correlated with Employment status and 1 other fieldsHigh correlation
Employment status is highly overall correlated with Employment lengthHigh correlation
Family member count is highly overall correlated with Children countHigh correlation
Gender is highly overall correlated with Job titleHigh correlation
Job title is highly overall correlated with Employment length and 1 other fieldsHigh correlation
Education level is highly imbalanced (50.6%) Imbalance
Dwelling is highly imbalanced (73.3%) Imbalance
Has an email is highly imbalanced (56.3%) Imbalance
Is high risk is highly imbalanced (87.5%) Imbalance
Job title has 9027 (31.0%) missing values Missing
ID has unique values Unique
Children count has 20143 (69.1%) zeros Zeros

Reproduction

Analysis started2025-07-01 11:56:40.291478
Analysis finished2025-07-01 11:56:54.942656
Duration14.65 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Unique 

Distinct29165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5078231.6
Minimum5008804
Maximum5150485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.0 KiB
2025-07-01T11:56:55.065498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5008804
5-th percentile5018455.4
Q15042047
median5074666
Q35114629
95-th percentile5146012.8
Maximum5150485
Range141681
Interquartile range (IQR)72582

Descriptive statistics

Standard deviation41824.001
Coefficient of variation (CV)0.008235938
Kurtosis-1.2095593
Mean5078231.6
Median Absolute Deviation (MAD)38051
Skewness0.084511077
Sum1.4810662 × 1011
Variance1.749247 × 109
MonotonicityNot monotonic
2025-07-01T11:56:55.280936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5037048 1
 
< 0.1%
5024541 1
 
< 0.1%
5112625 1
 
< 0.1%
5089643 1
 
< 0.1%
5091778 1
 
< 0.1%
5085636 1
 
< 0.1%
5025156 1
 
< 0.1%
5146530 1
 
< 0.1%
5062032 1
 
< 0.1%
5024207 1
 
< 0.1%
Other values (29155) 29155
> 99.9%
ValueCountFrequency (%)
5008804 1
< 0.1%
5008805 1
< 0.1%
5008806 1
< 0.1%
5008808 1
< 0.1%
5008810 1
< 0.1%
5008813 1
< 0.1%
5008814 1
< 0.1%
5008815 1
< 0.1%
5008819 1
< 0.1%
5008821 1
< 0.1%
ValueCountFrequency (%)
5150485 1
< 0.1%
5150482 1
< 0.1%
5150481 1
< 0.1%
5150480 1
< 0.1%
5150478 1
< 0.1%
5150477 1
< 0.1%
5150468 1
< 0.1%
5150465 1
< 0.1%
5150464 1
< 0.1%
5150463 1
< 0.1%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
F
19549 
M
9616 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29165
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowF
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
F 19549
67.0%
M 9616
33.0%

Length

2025-07-01T11:56:55.466131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T11:56:55.605486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
f 19549
67.0%
m 9616
33.0%

Most occurring characters

ValueCountFrequency (%)
F 19549
67.0%
M 9616
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 19549
67.0%
M 9616
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 19549
67.0%
M 9616
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 19549
67.0%
M 9616
33.0%

Has a car
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 KiB
False
18128 
True
11037 
ValueCountFrequency (%)
False 18128
62.2%
True 11037
37.8%
2025-07-01T11:56:55.686960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.6 KiB
True
19557 
False
9608 
ValueCountFrequency (%)
True 19557
67.1%
False 9608
32.9%
2025-07-01T11:56:55.758156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Children count
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43079033
Minimum0
Maximum19
Zeros20143
Zeros (%)69.1%
Negative0
Negative (%)0.0%
Memory size228.0 KiB
2025-07-01T11:56:56.102742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.74188219
Coefficient of variation (CV)1.7221422
Kurtosis23.798772
Mean0.43079033
Median Absolute Deviation (MAD)0
Skewness2.5929624
Sum12564
Variance0.55038919
MonotonicityNot monotonic
2025-07-01T11:56:56.229922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 20143
69.1%
1 6003
 
20.6%
2 2624
 
9.0%
3 323
 
1.1%
4 52
 
0.2%
5 15
 
0.1%
7 2
 
< 0.1%
14 2
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
0 20143
69.1%
1 6003
 
20.6%
2 2624
 
9.0%
3 323
 
1.1%
4 52
 
0.2%
5 15
 
0.1%
7 2
 
< 0.1%
14 2
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
19 1
 
< 0.1%
14 2
 
< 0.1%
7 2
 
< 0.1%
5 15
 
0.1%
4 52
 
0.2%
3 323
 
1.1%
2 2624
 
9.0%
1 6003
 
20.6%
0 20143
69.1%

Income
Real number (ℝ)

Distinct259
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186890.39
Minimum27000
Maximum1575000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.0 KiB
2025-07-01T11:56:56.410091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27000
5-th percentile76500
Q1121500
median157500
Q3225000
95-th percentile360000
Maximum1575000
Range1548000
Interquartile range (IQR)103500

Descriptive statistics

Standard deviation101409.64
Coefficient of variation (CV)0.54261563
Kurtosis18.289145
Mean186890.39
Median Absolute Deviation (MAD)45000
Skewness2.7571154
Sum5.4506581 × 109
Variance1.0283916 × 1010
MonotonicityNot monotonic
2025-07-01T11:56:56.659072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135000 3468
 
11.9%
180000 2487
 
8.5%
157500 2469
 
8.5%
225000 2373
 
8.1%
112500 2359
 
8.1%
202500 1781
 
6.1%
90000 1395
 
4.8%
270000 1344
 
4.6%
315000 795
 
2.7%
247500 686
 
2.4%
Other values (249) 10008
34.3%
ValueCountFrequency (%)
27000 1
 
< 0.1%
29250 3
 
< 0.1%
30150 3
 
< 0.1%
31500 15
0.1%
31531.5 3
 
< 0.1%
32400 3
 
< 0.1%
33300 9
< 0.1%
33750 1
 
< 0.1%
36000 3
 
< 0.1%
36900 6
 
< 0.1%
ValueCountFrequency (%)
1575000 7
 
< 0.1%
1350000 5
 
< 0.1%
1125000 3
 
< 0.1%
990000 3
 
< 0.1%
945000 3
 
< 0.1%
900000 28
0.1%
810000 13
< 0.1%
787500 1
 
< 0.1%
765000 5
 
< 0.1%
742500 4
 
< 0.1%

Employment status
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Working
15056 
Commercial associate
6801 
Pensioner
4920 
State servant
2381 
Student
 
7

Length

Max length20
Median length7
Mean length10.8587
Min length7

Characters and Unicode

Total characters316694
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWorking
2nd rowCommercial associate
3rd rowCommercial associate
4th rowCommercial associate
5th rowWorking

Common Values

ValueCountFrequency (%)
Working 15056
51.6%
Commercial associate 6801
23.3%
Pensioner 4920
 
16.9%
State servant 2381
 
8.2%
Student 7
 
< 0.1%

Length

2025-07-01T11:56:56.843492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T11:56:56.981567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
working 15056
39.3%
commercial 6801
17.7%
associate 6801
17.7%
pensioner 4920
 
12.8%
state 2381
 
6.2%
servant 2381
 
6.2%
student 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 33578
10.6%
o 33578
10.6%
r 29158
 
9.2%
e 28211
 
8.9%
n 27284
 
8.6%
a 25165
 
7.9%
s 20903
 
6.6%
W 15056
 
4.8%
k 15056
 
4.8%
g 15056
 
4.8%
Other values (11) 73649
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 316694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 33578
10.6%
o 33578
10.6%
r 29158
 
9.2%
e 28211
 
8.9%
n 27284
 
8.6%
a 25165
 
7.9%
s 20903
 
6.6%
W 15056
 
4.8%
k 15056
 
4.8%
g 15056
 
4.8%
Other values (11) 73649
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 316694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 33578
10.6%
o 33578
10.6%
r 29158
 
9.2%
e 28211
 
8.9%
n 27284
 
8.6%
a 25165
 
7.9%
s 20903
 
6.6%
W 15056
 
4.8%
k 15056
 
4.8%
g 15056
 
4.8%
Other values (11) 73649
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 316694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 33578
10.6%
o 33578
10.6%
r 29158
 
9.2%
e 28211
 
8.9%
n 27284
 
8.6%
a 25165
 
7.9%
s 20903
 
6.6%
W 15056
 
4.8%
k 15056
 
4.8%
g 15056
 
4.8%
Other values (11) 73649
23.3%

Education level
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
Secondary / secondary special
19803 
Higher education
7910 
Incomplete higher
 
1129
Lower secondary
 
298
Academic degree
 
25

Length

Max length29
Median length29
Mean length24.85462
Min length15

Characters and Unicode

Total characters724885
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecondary / secondary special
2nd rowHigher education
3rd rowSecondary / secondary special
4th rowHigher education
5th rowSecondary / secondary special

Common Values

ValueCountFrequency (%)
Secondary / secondary special 19803
67.9%
Higher education 7910
 
27.1%
Incomplete higher 1129
 
3.9%
Lower secondary 298
 
1.0%
Academic degree 25
 
0.1%

Length

2025-07-01T11:56:57.157442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T11:56:57.288742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
secondary 39904
40.7%
19803
20.2%
special 19803
20.2%
higher 9039
 
9.2%
education 7910
 
8.1%
incomplete 1129
 
1.2%
lower 298
 
0.3%
academic 25
 
< 0.1%
degree 25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 79312
10.9%
c 68796
9.5%
68771
9.5%
a 67642
9.3%
r 49266
 
6.8%
o 49241
 
6.8%
n 48943
 
6.8%
d 47864
 
6.6%
y 39904
 
5.5%
s 39904
 
5.5%
Other values (15) 165242
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 724885
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 79312
10.9%
c 68796
9.5%
68771
9.5%
a 67642
9.3%
r 49266
 
6.8%
o 49241
 
6.8%
n 48943
 
6.8%
d 47864
 
6.6%
y 39904
 
5.5%
s 39904
 
5.5%
Other values (15) 165242
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 724885
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 79312
10.9%
c 68796
9.5%
68771
9.5%
a 67642
9.3%
r 49266
 
6.8%
o 49241
 
6.8%
n 48943
 
6.8%
d 47864
 
6.6%
y 39904
 
5.5%
s 39904
 
5.5%
Other values (15) 165242
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 724885
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 79312
10.9%
c 68796
9.5%
68771
9.5%
a 67642
9.3%
r 49266
 
6.8%
o 49241
 
6.8%
n 48943
 
6.8%
d 47864
 
6.6%
y 39904
 
5.5%
s 39904
 
5.5%
Other values (15) 165242
22.8%

Marital status
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.8 MiB
Married
20044 
Single / not married
3864 
Civil marriage
2312 
Separated
 
1712
Widow
 
1233

Length

Max length20
Median length7
Mean length9.3100977
Min length5

Characters and Unicode

Total characters271529
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowSingle / not married
3rd rowMarried
4th rowSingle / not married
5th rowSeparated

Common Values

ValueCountFrequency (%)
Married 20044
68.7%
Single / not married 3864
 
13.2%
Civil marriage 2312
 
7.9%
Separated 1712
 
5.9%
Widow 1233
 
4.2%

Length

2025-07-01T11:56:57.471903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T11:56:57.624995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married 23908
55.5%
single 3864
 
9.0%
3864
 
9.0%
not 3864
 
9.0%
civil 2312
 
5.4%
marriage 2312
 
5.4%
separated 1712
 
4.0%
widow 1233
 
2.9%

Most occurring characters

ValueCountFrequency (%)
r 54152
19.9%
i 35941
13.2%
e 33508
12.3%
a 31956
11.8%
d 26853
9.9%
M 20044
 
7.4%
13904
 
5.1%
n 7728
 
2.8%
g 6176
 
2.3%
l 6176
 
2.3%
Other values (10) 35091
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 271529
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 54152
19.9%
i 35941
13.2%
e 33508
12.3%
a 31956
11.8%
d 26853
9.9%
M 20044
 
7.4%
13904
 
5.1%
n 7728
 
2.8%
g 6176
 
2.3%
l 6176
 
2.3%
Other values (10) 35091
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 271529
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 54152
19.9%
i 35941
13.2%
e 33508
12.3%
a 31956
11.8%
d 26853
9.9%
M 20044
 
7.4%
13904
 
5.1%
n 7728
 
2.8%
g 6176
 
2.3%
l 6176
 
2.3%
Other values (10) 35091
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 271529
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 54152
19.9%
i 35941
13.2%
e 33508
12.3%
a 31956
11.8%
d 26853
9.9%
M 20044
 
7.4%
13904
 
5.1%
n 7728
 
2.8%
g 6176
 
2.3%
l 6176
 
2.3%
Other values (10) 35091
12.9%

Dwelling
Categorical

Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
House / apartment
26059 
With parents
 
1406
Municipal apartment
 
912
Rented apartment
 
453
Office apartment
 
208

Length

Max length19
Median length17
Mean length16.790125
Min length12

Characters and Unicode

Total characters489684
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWith parents
2nd rowHouse / apartment
3rd rowHouse / apartment
4th rowHouse / apartment
5th rowHouse / apartment

Common Values

ValueCountFrequency (%)
House / apartment 26059
89.4%
With parents 1406
 
4.8%
Municipal apartment 912
 
3.1%
Rented apartment 453
 
1.6%
Office apartment 208
 
0.7%
Co-op apartment 127
 
0.4%

Length

2025-07-01T11:56:57.915070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T11:56:58.147449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
apartment 27759
32.9%
house 26059
30.9%
26059
30.9%
with 1406
 
1.7%
parents 1406
 
1.7%
municipal 912
 
1.1%
rented 453
 
0.5%
office 208
 
0.2%
co-op 127
 
0.2%

Most occurring characters

ValueCountFrequency (%)
t 58783
12.0%
a 57836
11.8%
e 56338
11.5%
55224
11.3%
n 30530
 
6.2%
p 30204
 
6.2%
r 29165
 
6.0%
m 27759
 
5.7%
s 27465
 
5.6%
u 26971
 
5.5%
Other values (15) 89409
18.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 489684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 58783
12.0%
a 57836
11.8%
e 56338
11.5%
55224
11.3%
n 30530
 
6.2%
p 30204
 
6.2%
r 29165
 
6.0%
m 27759
 
5.7%
s 27465
 
5.6%
u 26971
 
5.5%
Other values (15) 89409
18.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 489684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 58783
12.0%
a 57836
11.8%
e 56338
11.5%
55224
11.3%
n 30530
 
6.2%
p 30204
 
6.2%
r 29165
 
6.0%
m 27759
 
5.7%
s 27465
 
5.6%
u 26971
 
5.5%
Other values (15) 89409
18.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 489684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 58783
12.0%
a 57836
11.8%
e 56338
11.5%
55224
11.3%
n 30530
 
6.2%
p 30204
 
6.2%
r 29165
 
6.0%
m 27759
 
5.7%
s 27465
 
5.6%
u 26971
 
5.5%
Other values (15) 89409
18.3%

Age
Real number (ℝ)

Distinct6794
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-15979.477
Minimum-25152
Maximum-7705
Zeros0
Zeros (%)0.0%
Negative29165
Negative (%)100.0%
Memory size228.0 KiB
2025-07-01T11:56:58.451049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-25152
5-th percentile-23021
Q1-19444
median-15565
Q3-12475
95-th percentile-9873
Maximum-7705
Range17447
Interquartile range (IQR)6969

Descriptive statistics

Standard deviation4202.9975
Coefficient of variation (CV)-0.26302471
Kurtosis-1.0433005
Mean-15979.477
Median Absolute Deviation (MAD)3424
Skewness-0.18225185
Sum-4.6604146 × 108
Variance17665188
MonotonicityNot monotonic
2025-07-01T11:56:58.859626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12676 44
 
0.2%
-15519 44
 
0.2%
-16896 33
 
0.1%
-16053 26
 
0.1%
-16768 26
 
0.1%
-14400 25
 
0.1%
-14667 24
 
0.1%
-11126 24
 
0.1%
-22867 24
 
0.1%
-14122 24
 
0.1%
Other values (6784) 28871
99.0%
ValueCountFrequency (%)
-25152 1
 
< 0.1%
-25140 3
< 0.1%
-25099 1
 
< 0.1%
-25088 1
 
< 0.1%
-25010 2
< 0.1%
-24963 1
 
< 0.1%
-24946 3
< 0.1%
-24932 4
< 0.1%
-24914 3
< 0.1%
-24878 1
 
< 0.1%
ValueCountFrequency (%)
-7705 1
 
< 0.1%
-7723 1
 
< 0.1%
-7757 3
< 0.1%
-7959 2
< 0.1%
-7980 1
 
< 0.1%
-8041 4
< 0.1%
-8054 1
 
< 0.1%
-8056 2
< 0.1%
-8069 1
 
< 0.1%
-8076 2
< 0.1%

Employment length
Real number (ℝ)

High correlation 

Distinct3483
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59257.761
Minimum-15713
Maximum365243
Zeros0
Zeros (%)0.0%
Negative24257
Negative (%)83.2%
Memory size228.0 KiB
2025-07-01T11:56:59.158949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-15713
5-th percentile-7264
Q1-3153
median-1557
Q3-412
95-th percentile365243
Maximum365243
Range380956
Interquartile range (IQR)2741

Descriptive statistics

Standard deviation137655.88
Coefficient of variation (CV)2.3230018
Kurtosis1.1433571
Mean59257.761
Median Absolute Deviation (MAD)1309
Skewness1.7724256
Sum1.7282526 × 109
Variance1.8949142 × 1010
MonotonicityNot monotonic
2025-07-01T11:56:59.616837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365243 4908
 
16.8%
-401 61
 
0.2%
-200 55
 
0.2%
-2087 53
 
0.2%
-1539 51
 
0.2%
-1678 47
 
0.2%
-1081 47
 
0.2%
-2531 46
 
0.2%
-1160 45
 
0.2%
-309 44
 
0.2%
Other values (3473) 23808
81.6%
ValueCountFrequency (%)
-15713 1
 
< 0.1%
-15661 3
 
< 0.1%
-15227 1
 
< 0.1%
-15072 2
 
< 0.1%
-15038 13
< 0.1%
-14887 6
< 0.1%
-14810 6
< 0.1%
-14775 2
 
< 0.1%
-14536 4
 
< 0.1%
-14473 5
 
< 0.1%
ValueCountFrequency (%)
365243 4908
16.8%
-17 2
 
< 0.1%
-65 1
 
< 0.1%
-66 1
 
< 0.1%
-70 2
 
< 0.1%
-71 1
 
< 0.1%
-73 14
 
< 0.1%
-78 1
 
< 0.1%
-79 1
 
< 0.1%
-88 1
 
< 0.1%

Has a mobile phone
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
1
29165 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29165
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 29165
100.0%

Length

2025-07-01T11:57:00.064075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T11:57:00.299952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 29165
100.0%

Most occurring characters

ValueCountFrequency (%)
1 29165
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 29165
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 29165
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 29165
100.0%

Has a work phone
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
22623 
1
6542 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29165
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22623
77.6%
1 6542
 
22.4%

Length

2025-07-01T11:57:00.571630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T11:57:00.774358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 22623
77.6%
1 6542
 
22.4%

Most occurring characters

ValueCountFrequency (%)
0 22623
77.6%
1 6542
 
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22623
77.6%
1 6542
 
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22623
77.6%
1 6542
 
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22623
77.6%
1 6542
 
22.4%

Has a phone
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
20562 
1
8603 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29165
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 20562
70.5%
1 8603
29.5%

Length

2025-07-01T11:57:01.006425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T11:57:01.112062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 20562
70.5%
1 8603
29.5%

Most occurring characters

ValueCountFrequency (%)
0 20562
70.5%
1 8603
29.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20562
70.5%
1 8603
29.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20562
70.5%
1 8603
29.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20562
70.5%
1 8603
29.5%

Has an email
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
26532 
1
 
2633

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29165
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 26532
91.0%
1 2633
 
9.0%

Length

2025-07-01T11:57:01.237722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T11:57:01.351008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 26532
91.0%
1 2633
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 26532
91.0%
1 2633
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 26532
91.0%
1 2633
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 26532
91.0%
1 2633
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 26532
91.0%
1 2633
 
9.0%

Job title
Categorical

High correlation  Missing 

Distinct18
Distinct (%)0.1%
Missing9027
Missing (%)31.0%
Memory size1.8 MiB
Laborers
5004 
Core staff
2866 
Sales staff
2773 
Managers
2422 
Drivers
1722 
Other values (13)
5351 

Length

Max length21
Median length20
Mean length10.533916
Min length7

Characters and Unicode

Total characters212132
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCore staff
2nd rowAccountants
3rd rowLaborers
4th rowManagers
5th rowAccountants

Common Values

ValueCountFrequency (%)
Laborers 5004
17.2%
Core staff 2866
 
9.8%
Sales staff 2773
 
9.5%
Managers 2422
 
8.3%
Drivers 1722
 
5.9%
High skill tech staff 1133
 
3.9%
Accountants 998
 
3.4%
Medicine staff 956
 
3.3%
Cooking staff 521
 
1.8%
Security staff 464
 
1.6%
Other values (8) 1279
 
4.4%
(Missing) 9027
31.0%

Length

2025-07-01T11:57:01.504600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
staff 9672
29.7%
laborers 5142
15.8%
core 2866
 
8.8%
sales 2773
 
8.5%
managers 2422
 
7.4%
drivers 1722
 
5.3%
high 1133
 
3.5%
skill 1133
 
3.5%
tech 1133
 
3.5%
accountants 998
 
3.1%
Other values (13) 3567
 
11.0%

Most occurring characters

ValueCountFrequency (%)
a 24637
11.6%
s 24596
11.6%
r 20552
9.7%
e 20460
9.6%
f 19344
 
9.1%
t 13921
 
6.6%
12423
 
5.9%
o 10186
 
4.8%
i 8271
 
3.9%
n 6932
 
3.3%
Other values (26) 50810
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 212132
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 24637
11.6%
s 24596
11.6%
r 20552
9.7%
e 20460
9.6%
f 19344
 
9.1%
t 13921
 
6.6%
12423
 
5.9%
o 10186
 
4.8%
i 8271
 
3.9%
n 6932
 
3.3%
Other values (26) 50810
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 212132
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 24637
11.6%
s 24596
11.6%
r 20552
9.7%
e 20460
9.6%
f 19344
 
9.1%
t 13921
 
6.6%
12423
 
5.9%
o 10186
 
4.8%
i 8271
 
3.9%
n 6932
 
3.3%
Other values (26) 50810
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 212132
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 24637
11.6%
s 24596
11.6%
r 20552
9.7%
e 20460
9.6%
f 19344
 
9.1%
t 13921
 
6.6%
12423
 
5.9%
o 10186
 
4.8%
i 8271
 
3.9%
n 6932
 
3.3%
Other values (26) 50810
24.0%

Family member count
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1975313
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.0 KiB
2025-07-01T11:57:01.658127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum20
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91218872
Coefficient of variation (CV)0.41509704
Kurtosis8.6454749
Mean2.1975313
Median Absolute Deviation (MAD)0
Skewness1.3103351
Sum64091
Variance0.83208827
MonotonicityNot monotonic
2025-07-01T11:57:01.794992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 15552
53.3%
1 5613
 
19.2%
3 5121
 
17.6%
4 2503
 
8.6%
5 309
 
1.1%
6 48
 
0.2%
7 14
 
< 0.1%
9 2
 
< 0.1%
15 2
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
1 5613
 
19.2%
2 15552
53.3%
3 5121
 
17.6%
4 2503
 
8.6%
5 309
 
1.1%
6 48
 
0.2%
7 14
 
< 0.1%
9 2
 
< 0.1%
15 2
 
< 0.1%
20 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
15 2
 
< 0.1%
9 2
 
< 0.1%
7 14
 
< 0.1%
6 48
 
0.2%
5 309
 
1.1%
4 2503
 
8.6%
3 5121
 
17.6%
2 15552
53.3%
1 5613
 
19.2%

Account age
Real number (ℝ)

Distinct61
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26.137734
Minimum-60
Maximum0
Zeros247
Zeros (%)0.8%
Negative28918
Negative (%)99.2%
Memory size228.0 KiB
2025-07-01T11:57:01.982918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-55
Q1-39
median-24
Q3-12
95-th percentile-3
Maximum0
Range60
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.486702
Coefficient of variation (CV)-0.63076248
Kurtosis-1.0342853
Mean-26.137734
Median Absolute Deviation (MAD)14
Skewness-0.28850885
Sum-762307
Variance271.81133
MonotonicityNot monotonic
2025-07-01T11:57:02.208208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7 690
 
2.4%
-6 669
 
2.3%
-17 659
 
2.3%
-5 656
 
2.2%
-8 655
 
2.2%
-10 645
 
2.2%
-11 642
 
2.2%
-16 642
 
2.2%
-9 629
 
2.2%
-12 628
 
2.2%
Other values (51) 22650
77.7%
ValueCountFrequency (%)
-60 249
0.9%
-59 250
0.9%
-58 270
0.9%
-57 244
0.8%
-56 278
1.0%
-55 285
1.0%
-54 281
1.0%
-53 304
1.0%
-52 367
1.3%
-51 385
1.3%
ValueCountFrequency (%)
0 247
 
0.8%
-1 444
1.5%
-2 519
1.8%
-3 626
2.1%
-4 625
2.1%
-5 656
2.2%
-6 669
2.3%
-7 690
2.4%
-8 655
2.2%
-9 629
2.2%

Is high risk
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
0
28666 
1
 
499

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29165
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 28666
98.3%
1 499
 
1.7%

Length

2025-07-01T11:57:02.409455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-01T11:57:02.512291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 28666
98.3%
1 499
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 28666
98.3%
1 499
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 28666
98.3%
1 499
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 28666
98.3%
1 499
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 28666
98.3%
1 499
 
1.7%

Interactions

2025-07-01T11:56:52.915298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:43.292347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:44.941276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:47.120244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:48.714035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:50.366587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:51.661599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:53.082091image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:43.476711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:45.188410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:47.452662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:48.911739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:50.557142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:51.833405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:53.256372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:43.647275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:45.493334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:47.709971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:49.106203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:50.746273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:52.013630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:53.453392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:44.012576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:45.811670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:47.912182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:49.350159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:50.933531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:52.193358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:53.652134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:44.206955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:46.172833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:48.113067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:49.554570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:51.112941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:52.379721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:53.828817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:44.398814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:46.504745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:48.345186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:49.776008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:51.289927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:52.569812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:54.002781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:44.679488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:46.812524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:48.538041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:49.961375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:51.486538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-01T11:56:52.740921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-01T11:57:02.639088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Account ageAgeChildren countDwellingEducation levelEmployment lengthEmployment statusFamily member countGenderHas a carHas a phoneHas a propertyHas a work phoneHas an emailIDIncomeIs high riskJob titleMarital status
Account age1.0000.057-0.0050.0130.0120.0770.015-0.0270.0130.0440.0260.0140.0240.018-0.002-0.0260.0640.0250.030
Age0.0571.0000.3790.1110.123-0.2090.3790.3040.2080.1630.0660.1360.2030.1080.0530.0950.0180.0960.167
Children count-0.0050.3791.0000.0300.017-0.1420.0710.8250.0630.0860.0200.0070.0560.0040.0270.0430.0000.0580.078
Dwelling0.0130.1110.0301.0000.0510.1140.0620.0670.0830.0410.0400.2040.0390.0270.0320.0520.0080.0720.055
Education level0.0120.1230.0170.0511.0000.1470.0960.0280.0140.1060.0570.0420.0460.0950.0420.1090.0050.2040.047
Employment length0.077-0.209-0.1420.1140.1471.0000.998-0.1450.1750.1540.0100.0960.2420.086-0.008-0.1620.0001.0000.211
Employment status0.0150.3790.0710.0620.0960.9981.0000.1200.1900.1590.0120.0980.2540.1090.0470.0990.0120.1780.108
Family member count-0.0270.3040.8250.0670.028-0.1450.1201.0000.1050.1150.0250.0180.0550.0270.0260.0240.0060.0590.155
Gender0.0130.2080.0630.0830.0140.1750.1900.1051.0000.3600.0290.0470.0610.0000.0500.2010.0150.5580.164
Has a car0.0440.1630.0860.0410.1060.1540.1590.1150.3601.0000.0100.0110.0170.0160.0580.2060.0000.2720.152
Has a phone0.0260.0660.0200.0400.0570.0100.0120.0250.0290.0101.0000.0650.3120.0100.0650.0460.0000.0670.042
Has a property0.0140.1360.0070.2040.0420.0960.0980.0180.0470.0110.0651.0000.2100.0520.1840.0410.0250.0480.033
Has a work phone0.0240.2030.0560.0390.0460.2420.2540.0550.0610.0170.3120.2101.0000.0350.1270.0350.0000.0620.068
Has an email0.0180.1080.0040.0270.0950.0860.1090.0270.0000.0160.0100.0520.0351.0000.1650.0910.0000.0890.029
ID-0.0020.0530.0270.0320.042-0.0080.0470.0260.0500.0580.0650.1840.1270.1651.000-0.0220.0160.0640.042
Income-0.0260.0950.0430.0520.109-0.1620.0990.0240.2010.2060.0460.0410.0350.091-0.0221.0000.0000.1120.032
Is high risk0.0640.0180.0000.0080.0050.0000.0120.0060.0150.0000.0000.0250.0000.0000.0160.0001.0000.0280.022
Job title0.0250.0960.0580.0720.2041.0000.1780.0590.5580.2720.0670.0480.0620.0890.0640.1120.0281.0000.108
Marital status0.0300.1670.0780.0550.0470.2110.1080.1550.1640.1520.0420.0330.0680.0290.0420.0320.0220.1081.000

Missing values

2025-07-01T11:56:54.307610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-01T11:56:54.664223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDGenderHas a carHas a propertyChildren countIncomeEmployment statusEducation levelMarital statusDwellingAgeEmployment lengthHas a mobile phoneHas a work phoneHas a phoneHas an emailJob titleFamily member countAccount ageIs high risk
05037048MYY0135000.0WorkingSecondary / secondary specialMarriedWith parents-16271-31111000Core staff2.0-17.00
15044630FYN1135000.0Commercial associateHigher educationSingle / not marriedHouse / apartment-10130-16511000Accountants2.0-1.00
25079079FNY2180000.0Commercial associateSecondary / secondary specialMarriedHouse / apartment-12821-56571000Laborers4.0-38.00
35112872FYY0360000.0Commercial associateHigher educationSingle / not marriedHouse / apartment-20929-20461001Managers1.0-11.00
45105858FNN0270000.0WorkingSecondary / secondary specialSeparatedHouse / apartment-16207-5151010NaN1.0-41.00
55100411FYY0135000.0WorkingSecondary / secondary specialMarriedHouse / apartment-13251-38391100Accountants2.0-1.00
65022817MYY0202500.0WorkingSecondary / secondary specialMarriedHouse / apartment-17262-16171000Core staff2.0-16.00
75009811FNN1202500.0WorkingSecondary / secondary specialMarriedHouse / apartment-11813-32661110Sales staff3.0-21.00
85113922FNN090000.0PensionerSecondary / secondary specialSingle / not marriedMunicipal apartment-234783652431000NaN1.0-50.00
95021541FYN1306000.0WorkingHigher educationMarriedHouse / apartment-9310-16781000NaN3.0-13.00
IDGenderHas a carHas a propertyChildren countIncomeEmployment statusEducation levelMarital statusDwellingAgeEmployment lengthHas a mobile phoneHas a work phoneHas a phoneHas an emailJob titleFamily member countAccount ageIs high risk
291555021871FYY1315000.0State servantHigher educationWidowHouse / apartment-18233-4251011NaN2.0-30.00
291565009779MNN0135000.0WorkingSecondary / secondary specialSeparatedHouse / apartment-14118-31741000Laborers1.0-4.00
291575010913FYY081000.0PensionerHigher educationMarriedHouse / apartment-203993652431000NaN2.0-43.00
291585065502FYN1135000.0WorkingHigher educationMarriedMunicipal apartment-12523-24821000Managers3.0-13.00
291595091339FNY0135000.0Commercial associateSecondary / secondary specialMarriedHouse / apartment-11088-14471010Cooking staff2.0-3.00
291605067139FNY0112500.0PensionerSecondary / secondary specialSingle / not marriedHouse / apartment-234003652431011NaN1.0-5.00
291615029193FNY1135000.0Commercial associateSecondary / secondary specialMarriedHouse / apartment-15532-82561000Core staff3.0-24.00
291625047710FNY076500.0WorkingSecondary / secondary specialMarriedHouse / apartment-17782-32911110Managers2.0-29.00
291635009886FNY0157500.0PensionerSecondary / secondary specialCivil marriageHouse / apartment-216353652431010NaN2.0-37.00
291645062632FNY0585000.0Commercial associateSecondary / secondary specialMarriedHouse / apartment-18858-20101010NaN2.0-43.00